The Deep Parametric PDE Method: Application to Option Pricing

Autor:	Glau, Kathrin, Wunderlich, Linus
Rok vydání:	2020
Předmět:	Quantitative Finance - Computational Finance
Druh dokumentu:	Working Paper
Popis:	We propose the deep parametric PDE method to solve high-dimensional parametric partial differential equations. A single neural network approximates the solution of a whole family of PDEs after being trained without the need of sample solutions. As a practical application, we compute option prices in the multivariate Black-Scholes model. After a single training phase, the prices for different time, state and model parameters are available in milliseconds. We evaluate the accuracy in the price and a generalisation of the implied volatility with examples of up to 25 dimensions. A comparison with alternative machine learning approaches, confirms the effectiveness of the approach. Comment: Some examples can be reproduced in our Jupyter Notebook: https://github.com/LWunderlich/DeepPDE/blob/main/TwoAssetsExample/DeepParametricPDEExample.ipynb
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2012.06211 Zobrazit plný text záznamu View this record from Arxiv